Introduction – Data Mining Functionalities – Classification of Data Mining Systems – Data Mining Task Primitives – Major Issues in Data Mining. Data Preprocessing – Introduction - Data Cleaning – Data Integration and Transformation – Data Reduction - Data Discretization and Concept Hierarchy Generation.

The Apriori Algorithm-Finding Frequent Itemset using Candidate Generation - Generating Association Rules from Frequent Item sets – Improving the Efficiency of Apriori – Mining Various Kinds of Association Rules: Mining Multilevel Association rules – Mining Multidimensional Association Rules from Relational Databases and Data Warehouses.

Introduction to Classification and Prediction- Issues Regarding Classification and Prediction – Classification by Decision Tree Induction – Bayesian Classification – Rule based Classification – Other Classification Methods – Prediction- Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Increasing the accuracy – Model selection.

Introduction – Types of Data in Cluster Analysis – Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical Methods – Density based Methods – Grid based Methods – Model based Clustering Methods - Clustering High Dimensional Data- Constraint based Cluster Analysis - Outlier Analysis. Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data Mining – Text Mining – Mining the World Wide Web.

Overview- Data Warehouse Introduction – A Multidimensional Data Model – Data Warehouse Architecture – Data Warehouse Implementation – From Data Warehousing to Data Mining.